1. Technical Field
The present invention relates to anatomic object labeling and identification in medical images, and more particularly, to a system and method for labeling and identifying lymph nodes in medical images.
2. Discussion of the Related Art
The identification and evaluation of lymph nodes for cancer staging forms a large portion of a radiologist's workflow. Examples of this are described in J. P. Ko, E. A. Drucker, J. A. Shepard, C. F. Mountain, C. Dresler, B. Sabloff, and T. C. McLoud, “CT depiction of regional nodal stations for lung cancer staging”, AJR Am J Roentgenol. 2000 March; 174(3):775-82, and S. Aquino, M. Harisinghani, and B. Branstetter, “Lymph Node Anatomy and Imaging for Cancer Staging”, RSNA 2005. The information acquired when identifying and evaluating lymph nodes plays a central role in cancer diagnosis and treatment.
Previous approaches to automate the identification and evaluation of lymph nodes tend to focus on lymph node segmentation. Examples of this are found in D. M. Honea, Y. Ge, W. E. Snyder, P. F. Hemler, and D. J. Vining, “Lymph node segmentation using active contours”, SPIE Medical Imaging 1997: Image Processing, Vol. 3034. (1997), p. 265-273, J. Dornheim, H. Seim, B. Preim, I. Hertel, and G. Strauss, “Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models”, Medical Image Computing and Computer-Assisted Intervention—MICCAI 2006, Volume 4191/2006, pp. 904-911, and G. Unal, G. Slabaugh, A. Ess, A. Yezzi, T. Fang, J. Tyan, M. Requardt, R. Krieg, R. Seethamraju, M. Harisinghani, and R. Weissleder, “Semi-Automatic Lymph Node Segmentation in LN-MRI”, Proceedings of the IEEE Int. Conf. Image Processing, 2006.
The ability to segment lymph nodes provides the basis for quantitative information relating to size and shape. Such automated methods help reduce reader variability and errors, leading to more consistent measurements and assessments. Approaches that involve fast matching, directed contours, and spring-mass models have been applied along with various forms of shape priors. Although these methods show promising results, lymph node segmentation still remains a challenging task. Further, even with such tools available, radiologists must still manually provide anatomical labels for lymph nodes during staging and assessment.
Anatomical labels are assigned to groups of lymph nodes within specific regions of the body. They are critical for cancer staging since they help determine how far the cancer has spread. The labels are given to the lymph nodes depending upon their location relative to anatomical landmarks. Hence, in order to assign the proper label for a specific lymph node, the radiologist must examine the image and find these landmarks relative to the lymph node. Additionally, for follow up cases, specific lymph nodes must be found and compared to their appearance in prior scans. These requirements impact the workload of radiologists both in the time required to find nearby landmarks and in the time for adding the information within, for example, a dictation system.
Automated anatomic labeling of medical images has been used for various purposes other than lymph nodes. In the case of the airways, it has been used to label specific branches and for follow-up studies for virtual bronchoscopy. Examples of this are found in K. Mori, J. Hasegawa, Y. Suenaga, J. Toriwaki, “Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system”, IEEE Transaction in Medical Imaging, vol. 19, no. 2, February 2000, p. 103-114, J. Tschirren, G. McLennan, K. Palágyi, E. A. Hoffman, and M. Sonka, “Matching and anatomical labeling of human airway tree”, IEEE Transactions in Medical Imaging, vol. 24, no. 12, December 2005, p. 1540-1547, and J. Kaftan, A. P. Kiraly, D. P. Naidich, C. L. Novak, “A Novel Multi-Purpose Tree and Path Matching Algorithm with Application to Airway Trees”, SPIE Medical Imaging 2006, vol. 6143 (2006).
Anatomical landmark identification has been used to improve colon segmentation. An example of this is found in J. J. Nappi, A. H. Dachman, H. Abraham, P. MacEneaney, H. Yoshida, “Automated Knowledge-Guided Segmentation of Colonic Walls for Computerized Detection of Polyps in CT Colonography”, Journal of Computer Assisted Tomography, 26(4):493-504, July/August 2002. Rib labeling of computed tomography (CT) datasets has been used to reduce a radiologist's workflow. An example of this is described in H. Shen, L. Liang, M. Shao, and S. Qing, “Tracing Based Segmentation for the Labeling of Individual Rib Structures in Chest CT Volume Data”, MICCAI 2004, vol. 3217, p. 967-974, 2004.
In J. P. Ko, E. A. Drucker, J. A. Shepard, C. F. Mountain, C. Dresler, B. Sabloff, and T. C. McLoud, “CT depiction of regional nodal stations for lung cancer staging”, AJR Am J Roentgenol. 2000 March; 174(3):775-82, it is described how lymph nodes may be divided into four major groups, e.g., superior mediastinal nodes, aortic nodes, inferior mediastinal nodes and N1 nodes. FIG. 1 illustrates locations of these groups in relation to the airways and the aorta, while FIG. 2 shows marked nodal locations and airways within an actual dataset. For example, in FIG. 1(a), 1=superior mediastinal nodes, 2=aortic nodes, 3=inferior mediastinal nodes, 4=N1 nodes, A=aorta, PA=pulmonary artery, and C=carina. In FIG. 1(b), some of the nodes specific to group 1 are labeled as, A=highest mediastinal, B=upper paratracheal, and C=lower paratracheal. Lymph nodes are further divided into 14 sub-groups, or stations, including hilar, interlobar, lobar, and so forth as listed in a table shown in FIG. 3.
Currently, radiologists reading staging exams must identify and assess major lymph nodes to document abnormalities. The correct determination of a node's label is critical for accurate disease staging, and consequently determining the best treatment options for the patient. However, this process is time consuming and occasionally inaccurate. Accordingly, there is a need for a technique that is capable of quickly and accurately labeling lymph nodes in medical images.